基于优化离散差分进化算法的压缩感知信号重构

doi:10.13229/j.cnki.jdxbgxb20200133

Abstract

Abstract:

In order to overcome the shortcomings of traditional compressed sensing reconstruction algorithms, such as heavy dependence on sparseness and low reconstruction accuracy, an Optimized Discrete Differential Evolution (ODDE) algorithm is proposed to analyze the evolutionary population. ODDE can improve the learning and evolution of the population while realizing effective clustering. The differential evolution particle coding method and evolution mechanism are redefined, and the ODDE is applied to the compressed sensing reconstruction method. The sparse unknown signal is equivalent to the particle coding, and the sparse signal is reconstructed accurately through population iterative evolution. The simulation results show that compared with traditional reconstruction algorithms such as StOMP, the reconstruction accuracy of this method is significantly improved and the reconstruction time is reduced.

Key words: computer application, sparsity, discrete difference, intelligent evolutionary algorithms, compressed sensing, sparse reconstruction

CLC Number:

TP393

Zhou-zhou LIU,Qian-yun ZHANG,Xin-hua MA,Han PENG. Compressed sensing signal reconstruction based on optimized discrete differential evolution algorithm[J].Journal of Jilin University(Engineering and Technology Edition), 2021, 51(6): 2246-2252.

Figures/Tables 6

Fig.1

Table 1

Comparison of evaluation indicators forfour reconstruction algorithms"

信号	指标	算法
信号	指标	ODDE	文献［2］	文献［8］	StOMP
1	$R S ˉ$ /%	100	97.3	50.2	17.3
	$T ˉ$ /s	0.17	0.38	0.28	0.37
	$R E$	0.007	0.012	1.845	3.330
2	$R S ˉ$ /%	100	98.2	49.2	15.3
	$T ˉ$ /s	0.25	0.44	0.36	0.82
	$R E$	0.014	0.018	1.038	2.143
3	$R S ˉ$ /%	100	97.6	55.7	10.8
	$T ˉ$ /s	0.56	0.92	0.66	1.27
	$R E$	0.013	0.017	1.836	3.045

Table 1

Fig.2

Fig.3

Fig.4

Fig.5

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